About

I am a member of the Machine Learning and Optimization and the Algorithms and Data Sciences Group at Microsoft Research, Bangalore, India. My research interests are in machine learning, large-scale (non-convex optimization), and statistical learning theory. I am also interested in applications of machine learning to privacy, computer vision, text mining and natural language processing. I completed my PhD at the University of Texas at Austin under Prof. Inderjit S. Dhillon.

I am also an adjunct faculty member at IIT Kanpur.


Professional Services

  • Organization:
    • Program Co-Chair, IKDD Conference on Data Sciences (CoDS), 2016.
    • Organizer, Machine Learning Summer School, Microsoft Research, 2015.
    • Organizer, Mysore Park Workshop on Machine Learning, Mysore, India, 2012.
  • Program Committee/Area Chair
    • COLT 2015, 2016
    • NIPS 2012, 2013, 2016

Students

Over the years, I have been very lucky to have worked with some amazing postdocs/interns/research fellows.

PostDocs:

  • Purushottam Kar, 2013-2015 (Asst. Prof., IIT Kanpur)
  • Nagarajan Natarajan, 2015- (Postdoc, MSR India)

Interns:

  • Elena-Madalina Persu, Summer’2015. (Phd Student, MIT)
  • Gautam Kamath, Summer’2015. (Phd Student, MIT)
  • Harikrishna Narasimhan, Summer’2014. (Postdoc, Harvard University)
  • Praneeth Netrapalli, Summer’2012, 2014. (Postdoc, Microsoft Research New England)
  • Srinadh Bhojanapalli, Summer’2013, 2014. (Assistant Professor, TTI Chicago)
  • Pravesh Kothari, Summer’2014. (Phd Student, UT Austin)
  • Purushottam Kar, Summer’2012. (Assistant Professor, IIT Kanpur)
  • Sivakanth Gopi, Summer’2012. (Phd Student, Princeton University)
  • Ankan Saha, Summer’2011. (Software Engineer, LinkedIn)
  • Saurabh Gupta, Summer’2011. (Phd Student, UC Berkeley)

Research Fellows:

  • Kush Bhatia, 2014-2016. (PhD Student, UC Berkeley)
  • Yeshwanth Cherapanamjeri, 2015-. (RF, MSR India)
  • Raajay Viswanathan, 2011-2013. (PhD Student, UWisc Madison)

Projects

Provable Non-convex Optimization for Machine Learning Problems

Established: April 4, 2014

In this work, we explore theoretical properties of simple non-convex optimization methods for problems that feature prominently in several important areas such as recommendation systems, compressive sensing, computer vision etc. Talks: Provable Non-convex Optimization for Machine Learning. Summer School on…

Publications

2016

2015

2014

2013

Memory Limited, Streaming PCA
Ioannis Mitliagkas, Constantine Caramanis, Prateek Jain, in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States., December 1, 2013, View abstract, View external link

2012

2011

2010

Projects

Panel Q and A Link description

Panel Q and A

Date

July 27, 2015

Speakers

Prateek Jain, Chin-Jen Lin, Aditya Gopalan, Suvrit Sra, and Stefanie Jegelka

Affiliation

Microsoft, National Taiwan University, IISc, Max Planck Institute for Intelligent Systems, UC Berkeley

Other

Projects

  • Non-convex Optimization for High-Dimensional Statistics
  • Matrix Completion and Low-rank Matrix Recovery
  • Compressive Sensing
  • Learning with Non-decomposable Loss Functions
  • Differential Privacy for Machine Learning
  • Distance Metric Learning